Saliency Detection
130 papers with code • 7 benchmarks • 13 datasets
Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.
Source: An Unsupervised Game-Theoretic Approach to Saliency Detection
Benchmarks
These leaderboards are used to track progress in Saliency Detection
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Libraries
Use these libraries to find Saliency Detection models and implementations
PASCAL Context
PASCAL-S
DUTS
HKU-IS
DUT-OMRON
iSUN
CAT2000
ECSSD
ReDWeb-S
Lytro Illum
Subtasks
Latest papers
Finding Visual Saliency in Continuous Spike Stream
bit-vision/svs
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Our framework exhibits a substantial margin of improvement in capturing and highlighting visual saliency in the spike stream, which not only provides a new perspective for spike-based saliency segmentation but also shows a new paradigm for full SNN-based transformer models.
SAMF: Small-Area-Aware Multi-focus Image Fusion for Object Detection
Existing multi-focus image fusion (MFIF) methods often fail to preserve the uncertain transition region and detect small focus areas within large defocused regions accurately.
SASA: Saliency-Aware Self-Adaptive Snapshot Compressive Imaging
The ability of snapshot compressive imaging (SCI) systems to efficiently capture high-dimensional (HD) data depends on the advent of novel optical designs to sample the HD data as two-dimensional (2D) compressed measurements.
An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos
In this work, we present an integrated system for spatiotemporal summarization of 360-degrees videos.
Joint Correcting and Refinement for Balanced Low-Light Image Enhancement
woshiyll/jcrnet
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Specifically, the proposed method, so-called Joint Correcting and Refinement Network (JCRNet), which mainly consists of three stages to balance brightness, color, and illumination of enhancement.
NPF-200: A Multi-Modal Eye Fixation Dataset and Method for Non-Photorealistic Videos
yangziyu/npf200
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Non-photorealistic videos are in demand with the wave of the metaverse, but lack of sufficient research studies.
LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training
silky1708/locate
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We demonstrate the effectiveness of our approach, named LOCATE, on multiple standard video object segmentation, image saliency detection, and object segmentation benchmarks, achieving results on par with and, in many cases surpassing state-of-the-art methods.
Decoupled Diffusion Models: Simultaneous Image to Zero and Zero to Noise
We propose decoupled diffusion models (DDMs) for high-quality (un)conditioned image generation in less than 10 function evaluations.
Explainable Image Quality Assessment for Medical Imaging
We apply a variety of techniques to measure the faithfulness of the saliency detectors, and our explainable pipeline relies on NormGrad, an algorithm which can efficiently localise image quality issues with saliency maps of the classifier.
ScanDMM: A Deep Markov Model of Scanpath Prediction for 360deg Images
xiangjiesui/scandmm
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CVPR 2023
Scanpath prediction for 360deg images aims to produce dynamic gaze behaviors based on the human visual perception mechanism.
